Your GRC Platform Organizes the Evidence. But What Can the Evidence Actually Prove?

Key Takeaways
Treat every artifact in your GRC platform as a claim with boundaries: record its criterion, scope, period and source before you rely on it.
A green dashboard tile proves only what the underlying test examined. Check population coverage before you report a control as effective.
Rank compliance evidence by independence and reliability. Self-attestations sit at the bottom; independent testing against defined criteria sits at the top.
Run the five-question sufficiency test (criterion, population, period, source, reliability) on every key control before it reaches a board report or a customer assurance response.
Split evidence ownership across the Three Lines: control owners produce it, the second line defines and challenges it, internal audit gives independent assurance over it.
Monitor evidence quality itself with KRIs such as coverage ratio, evidence age and silent integration failures.
Never accept a vendor SOC 2 report or ISO certificate at face value. Read the scope, period, exceptions and the name of the auditor.

In spring 2026, an anonymous whistleblower writing as “DeepDelver” alleged that Delve, a venture-backed compliance automation startup, had supplied customers with pre-filled evidence and steered them to audit firms that rubber-stamped reports. According to IANS Research’s review of the allegations, a leaked spreadsheet showed near-identical boilerplate in 493 of 494 SOC 2 reports. Delve denied the claims, stating that it does not issue compliance reports and that independent auditors own their opinions. Whatever the final findings, the episode put one question on every risk committee agenda.

How much of our compliance evidence actually proves anything? Hundreds of companies had evidence folders that looked complete, controls marked effective and reports that looked professional. The repository was organized. What it could prove was a different matter. This article explains why that gap exists, ranks compliance evidence by what it can sustain, and gives risk and compliance teams a sufficiency test, an ownership model and a set of KRIs they can apply to their own GRC platform this quarter.

Governance, risk and compliance platforms have become central to the way organizations manage cybersecurity, regulatory obligations and enterprise risk. Modern GRC systems can connect with cloud environments, identity platforms, ticketing systems, policy repositories and other enterprise technologies, allowing organizations to automate evidence collection, map controls across frameworks, monitor selected control conditions and maintain detailed records of risk and compliance activity.

These capabilities have changed the operating model for risk teams. Evidence that once had to be requested individually from control owners can now be collected through system integrations or retained within centralized repositories. Policies, access reviews, security configurations, vendor assessments and risk registers can be associated with specific controls and requirements. For organizations working across multiple standards or regulatory frameworks, this can reduce duplication and provide a more consistent view of evidence.

Risk Publishing’s 2026 analysis of GRC platforms reflects that development. Automated evidence collection, continuous control monitoring, framework mapping, audit trails, risk dashboards and third-party risk functions feature prominently in evaluations of leading GRC technologies (Ekai, 2026a).

As these capabilities expand, however, organizations face a separate question that technology alone cannot resolve: whether the evidence collected is sufficient to establish a particular conclusion against defined criteria.

A platform may establish that a document exists, that a configuration was detected or that a control owner completed an assigned activity. It may also map the same artifact against several requirements. Those functions do not, by themselves, determine whether the evidence demonstrates conformity with a standard, satisfies a technical criterion or adequately represents the environment being assessed.

Compliance Evidence Depends on Context

The significance of evidence depends on the question being asked of it. An access-control policy may establish that an organization has documented requirements for user access. An identity-system integration may provide information about multi-factor authentication settings, while access-review records may demonstrate that periodic reviews occurred. Each artifact may be relevant, but its significance within an assessment depends on scope, timing, completeness and the criteria against which it is evaluated.

An evaluator may need to determine whether the evidence applies to all systems included in scope, whether privileged accounts are covered, whether exceptions exist, whether documented procedures correspond with observed operating conditions and whether the records represent an appropriate period. Information stored in a GRC platform may therefore be accurate while remaining insufficient for a particular conclusion.

The National Institute of Standards and Technology’s Cybersecurity Framework 2.0 illustrates why this distinction matters. The framework provides a taxonomy of high-level cybersecurity outcomes organizations can use to understand, assess, prioritize and communicate cybersecurity risk. NIST also makes clear that the framework does not prescribe how those outcomes must be achieved (Pascoe et al., 2024). Our guide on identifying cybersecurity risks with NIST shows how those outcomes translate into practice.

A GRC platform may map controls and artifacts to those outcomes, but the mapping itself does not independently establish that an outcome has been achieved within the environment under examination. The same principle applies to risk registers, vulnerability reports, vendor questionnaires, training records, policies, incident logs and technical test results. Evidence becomes meaningful when its origin, scope, period and relationship to the applicable criterion are understood.

Dashboards Simplify Information, but They Do Not Settle the Assurance Question

One of the strengths of GRC technology is its ability to consolidate large volumes of information. Executive dashboards can combine control status, open issues, vendor assessments, technical findings, policy records and automated monitoring into a single view. This can provide management with useful visibility, but it can also create the impression that different categories of evidence carry equivalent weight. Good risk reporting practice keeps those categories visible rather than blending them into one status colour.

Consider a control shown as passing because an integration detects that multi-factor authentication is enabled. The result may accurately reflect the condition tested by the platform. It may not establish whether every relevant identity, administrative path, service account or application within a particular assessment scope is subject to the same requirement.

The limitation lies in scope rather than necessarily in the accuracy of the automated result. The conclusion that can be drawn from it is bounded by what the test actually examined.

This becomes more important as organizations rely on aggregated compliance indicators. A management attestation, an automated configuration check, a technical test and an independent assessment are all forms of evidence, but they arise from different processes and answer different questions. Treating them as interchangeable can obscure the limitations of the underlying information.

For risk leaders, the practical issue is therefore not only whether evidence is collected, but whether its context is preserved. Organizations need to understand where an artifact originated, what it covers, how current it is, whether it has been independently evaluated and what conclusions it can reasonably sustain.

Independent Assessment Serves a Different Function

Independent assessment does not replace GRC technology, nor does it perform the same function. GRC platforms provide infrastructure for records, workflows, monitoring and evidence management. Independent assessment evaluates evidence against defined criteria within an established scope.

Depending on the engagement, that evaluation may involve examination of records and documentation, interviews, sampling, observation of systems, configuration review, technical testing and other procedures relevant to the criteria being examined. The resulting conclusion is tied to the requirements assessed, the evidence obtained and the boundaries within which the work was performed. NIST SP 800-53A formalizes this by defining assessment methods (examine, interview, test) and the depth and coverage each one must reach.

This distinction matters when organizations make external assurance claims. A customer asking whether a security requirement has been independently evaluated is asking a different question from whether an internal GRC platform shows the associated control as active. Both forms of information can be useful, but they should not be treated as equivalent.

The distinction also matters for boards and senior executives. GRC dashboards provide a broad view of risk and compliance conditions, while independent assessments provide conclusions bounded by defined criteria and evaluation procedures. Understanding the difference is essential to interpreting assurance accurately.

What Compliance Evidence Can Prove: A Practical Hierarchy

The sections above make one point repeatedly: different evidence answers different questions. The table below turns that point into a working reference. It ranks common GRC artifacts from least to most independent, drawing on the evidence principles in ISO 19011:2018 guidelines for auditing management systems and the IIA Global Internal Audit Standards, which require conclusions to rest on information that is relevant, reliable and sufficient. Use it when deciding how much weight a record deserves in a compliance risk assessment.

Evidence type	Source and independence	What it can prove	What it cannot prove on its own
Policy or procedure document	Management; no independence	A requirement has been designed and approved	That anyone follows it
Management attestation or control self-assessment	Control owner; self-reported	The owner asserts the control operated	That the assertion is accurate or complete
Vendor security questionnaire	Vendor; self-reported and commercially motivated	The vendor’s stated practices at a point in time	Actual control operation at the vendor
Automated configuration check (GRC integration)	System; objective but narrow	The setting the connector queried, for the population it can see	Identities, systems or access paths outside the connector
System-generated report or log	System output, extracted by management	Events or transactions recorded in the system	Its own completeness and accuracy, unless extraction and IT general controls are tested
Security rating score	Third party; outside-in methodology	Externally observable signals	Internal controls, governance or remediation quality
Independent technical test (for example, a penetration test)	Qualified tester; independent within scope	Exploitable weaknesses in tested systems during the test window	Systems, periods or attack paths outside the scope
External attestation or certification (SOC 2 Type II, ISO/IEC 27001)	Independent auditor or certification body	Conformity or operating effectiveness against defined criteria, for a defined scope and period	Anything outside scope, carve-outs or the period; quality depends on the auditor
Internal audit engagement	Third line; organizationally independent	Assurance on design and operating effectiveness against the engagement criteria	Areas outside the audit plan

Two caveats apply. First, independence raises the weight of evidence but does not guarantee its quality, which is exactly what the Delve allegations put to the test. An external attestation is only as good as the procedures behind it, so read who signed it and whether exceptions were reported; the cost and scope of a SOC 2 audit tells you a lot about how deep the testing went. Second, evidence types combine. An automated check corroborated by a sample-based independent test sustains a far stronger conclusion than either one alone.

ISO/IEC 27001 Shows Why Defined Criteria Matter

ISO/IEC 27001 provides a useful illustration. The standard specifies requirements for an information security management system and establishes a structured basis for managing information-security risks (International Organization for Standardization [ISO], 2022).

Organizations frequently use GRC platforms to maintain policies, risk records, control mappings, review documentation and other evidence associated with an ISMS. Those functions can make information-security governance more structured, particularly when an organization operates across overlapping frameworks. They do not, however, establish conformity with ISO/IEC 27001 on their own.

A document mapped to a requirement may be relevant, but the existence of the mapping does not determine whether the evidence is sufficient. One artifact may also relate to several requirements without carrying identical evidentiary significance for each one. The conclusion depends on the requirement being examined and the context in which the evidence is evaluated.

Cross-framework mapping is therefore best understood as an organizational capability rather than an assurance outcome. It can identify relationships between requirements and available evidence, while independent evaluation addresses what those relationships demonstrate within the defined scope.

AI Governance Is Making Evidence More Complex

Artificial intelligence adds another layer of complexity because AI governance depends heavily on accountability, traceability, risk classification, monitoring, measurement and documented governance processes. GRC platforms are increasingly incorporating AI inventories, governance workflows, questionnaires and risk-management functions intended to give organizations greater visibility over their use of AI.

ISO/IEC 42001:2023 specifies requirements for establishing, implementing, maintaining and continually improving an artificial intelligence management system. It applies to organizations providing or using AI-based products and services and establishes a management-system structure for addressing AI-related risks and opportunities (ISO, 2023).

Organizations may use GRC technology to maintain AI inventories, assign ownership, record risk decisions and retain evidence associated with those requirements. As with information-security frameworks, the existence of those records does not independently determine whether governance arrangements operate as represented.

NIST’s Artificial Intelligence Risk Management Framework similarly emphasizes measurement, testing, evaluation, verification, validation and documentation. Its Measure function notes that independent review can increase the effectiveness of testing while reducing potential internal bias and conflicts of interest (Tabassi, 2023). Our NIST AI RMF implementation guide maps those Measure outputs to evidence an assessor can test.

For organizations adopting AI at scale, this changes the governance question. The issue is no longer limited to whether policies and inventories exist. Legal, compliance, security and risk functions increasingly need to determine whether sufficient evidence exists to demonstrate how governance mechanisms operate across the systems and processes included in scope.

That becomes particularly relevant as organizations contend with externally sourced AI technologies, embedded AI functionality and AI use occurring outside formally established enterprise processes. A GRC system can provide a structured environment for recording these conditions, but the quality of assurance still depends on the underlying evidence and how that evidence is evaluated.

Third-Party Risk Creates Similar Evidentiary Challenges

The same boundaries appear in third-party risk management. Organizations now depend extensively on SaaS providers, cloud infrastructure, software suppliers, data processors and AI platforms. Risk teams have responded by creating increasingly structured third-party governance programs, often using TPRM and GRC systems to centralize vendor questionnaires, security documentation, contracts, risk classifications, monitoring alerts and external assurance records.

Risk Publishing’s 2026 third-party risk framework emphasizes vendor inventories, risk classification, due diligence, monitoring and risk indicators as important elements of a structured third-party risk-management lifecycle (Ekai, 2026b).

NIST has similarly identified reduced visibility into technology supply chains as a material cybersecurity challenge. Its Cybersecurity Supply Chain Risk Management Practices for Systems and Organizations provides a structured approach for identifying and assessing cybersecurity risks associated with suppliers, products and services (Boyens et al., 2024).

Centralizing vendor information can create greater consistency, but the documents collected do not represent equivalent forms of assurance. A vendor questionnaire generally contains representations supplied by the vendor. A security-rating score reflects a particular methodology and set of observations. A certification is associated with defined criteria and scope. A technical assessment reflects testing conducted within established boundaries and during a particular period.

Risk teams therefore need to understand the characteristics of the evidence on which they rely. Source, scope, independence, testing period and limitations all affect what can reasonably be concluded from a third-party record.

This becomes particularly important when automated vendor scores or dashboard statuses are used as shorthand for complex risk decisions. Technology can create consistency in how those decisions are administered, but the underlying evidence should remain distinguishable.

Technical Testing Provides a Different Category of Evidence

Technical security testing offers another example of why evidence categories matter. A GRC platform may record that testing took place, retain the resulting report and associate findings with security controls or risk categories. It may also track the status of findings over time.

Those records are useful for governance purposes, but they are not equivalent to the testing activity that generated them.

A technical security assessment is conducted within a defined scope and according to established procedures, such as those described in NIST SP 800-115, Technical Guide to Information Security Testing and Assessment. Its findings reflect the systems examined, methods used, conditions observed and period during which the work occurred. Interpreting those findings therefore requires an understanding of those boundaries.

The broader implication for GRC practitioners is that storing evidence and producing evidence are different activities. A platform can preserve technical test results and connect them to governance processes, but mapping several records to the same control does not make those records equivalent in evidentiary terms.

A Five-Question Sufficiency Test for Compliance Evidence

Before a control’s evidence goes into a board report, a regulator response or a customer security questionnaire, put it through five questions. They compress the evidence requirements of ISO 19011, the IIA Standards and PCAOB AS 1105 as amended in 2024. For audits of fiscal years beginning on or after December 15, 2025, those amendments require auditors to evaluate the reliability of electronic information used in technology-assisted analysis, including the company’s IT general controls and automated controls over that information. If external auditors must ask these questions of your data, your second line should ask them first.

#	Question	Why it matters	Red flag
1. Criterion	Which exact requirement, clause or control objective is this evidence meant to satisfy?	Evidence is only sufficient relative to a criterion. One artifact mapped to five requirements rarely satisfies all five equally.	The mapping exists, but nobody can say which part of the requirement the artifact addresses.
2. Population	Does it cover the full in-scope population of systems, identities, vendors or transactions?	A pass on a subset gets silently reported as a pass on the whole.	The connector covers SSO users only; service accounts and local admins are invisible to it.
3. Period	Does it cover the period the conclusion refers to, or only a point in time?	Operating effectiveness is a period claim. A screenshot is a point-in-time claim.	Evidence dated after the period end, or a single snapshot supporting a 12-month claim.
4. Source and independence	Who produced it, and could they be mistaken or motivated to present it favorably?	Self-reported evidence carries less weight than evidence obtained independently.	Vendor-supplied templates or pre-filled evidence stored as if your own systems generated it.
5. Reliability	Is the underlying data complete and accurate, and are the controls over its generation tested?	An accurate-looking report built on incomplete data produces confident wrong answers.	Integration logic is undocumented, and nobody has tested IT general controls over the source system.

Worked Example: The MFA Control That Was 85% Green

A mid-sized financial services firm reports its “MFA enforced for all users” control as passing. The GRC integration queries the identity provider and confirms that conditional access requires MFA for every account it can see: 1,050 SSO identities. Apply the population question and the picture changes. The in-scope population in the asset and identity inventory is 1,240 identities, including 140 service accounts and 50 local administrator accounts that never pass through the identity provider. Coverage is 1,050 ÷ 1,240 = 84.7%.

The dashboard is accurate about what it tested and wrong about the control. The fix is not a better dashboard. It is a documented population reconciliation, a compensating control for the 190 identities outside the connector (for example, vaulting and rotation for service accounts), and a quarterly check that the connector’s scope still matches the inventory. Log the gap in your control risk assessment until the compensating control is tested.

Who Owns Compliance Evidence Quality Across the Three Lines

Evidence quality fails most often because nobody owns it. The GRC administrator owns the tool, control owners upload files, and internal audit samples at year end. The IIA Three Lines Model offers a cleaner split, and our Three Lines Model implementation guide covers the governance mechanics in more depth.

Activity	First line (control owners)	Second line (risk and compliance)	Third line (internal audit)	GRC platform administrator
Define evidence requirements per control (criterion, population, period)	C	A/R	C	I
Produce and upload evidence	A/R	I	I	C
Configure integrations and document test logic and scope	C	A	I	R
Reconcile integration scope to asset and identity inventories (quarterly)	R	A	I	R
Challenge evidence sufficiency before external reporting	C	A/R	I	I
Independent assurance on reliance placed on automated evidence	I	C	A/R	C
Report evidence quality KRIs to the risk committee	I	A/R	C	C

Key: R = Responsible, A = Accountable, C = Consulted, I = Informed. The row that matters most is the second from the bottom. If internal audit has never tested the logic behind your automated tests, you are relying on unaudited code for audit conclusions. Add GRC integrations and test scripts to your audit universe.

KRIs That Show Whether Your Compliance Evidence Can Be Trusted

If evidence quality matters, measure it. The indicators below monitor the evidence itself rather than the controls it supports, in line with the monitoring and review principle of ISO 31000:2018. The thresholds are illustrative starting points; calibrate them to your risk appetite and build them out with our key risk indicators template.

KRI	Definition	Green	Amber	Red
Evidence coverage ratio	In-scope population covered by the automated test ÷ total in-scope population, for key controls	95% or more	85% to 94%	Below 85%
Stale evidence rate	Key control evidence older than the reporting period ÷ total key control evidence	5% or less	6% to 15%	Above 15%
Silent integration failures	Connectors that failed or returned no data for more than 7 days without raising an alert	0	1	2 or more
Unverified passing controls	Key controls shown as effective with no independent test in the last 12 months ÷ key controls	10% or less	11% to 25%	Above 25%
Self-attested critical vendors	Critical vendors assessed only by questionnaire, with no independent attestation or testing	0	1 to 2	3 or more
Orphaned evidence	Artifacts with no named owner, source system or collection date ÷ total artifacts	2% or less	3% to 10%	Above 10%

Escalation rule: any red KRI goes to the risk committee with a named owner and a remediation date, and two consecutive ambers are treated as red. Report these alongside control status so the board sees evidence quality next to the conclusion it qualifies. The MFA example above would have tripped the coverage KRI at 84.7% long before an auditor or a customer found the gap.

Evidence Provenance Is Becoming a Governance Issue

As organizations increase automation, evidence provenance is likely to become a more important component of risk governance. Risk leaders need to know what criterion an artifact relates to, what systems and business units are represented, when the evidence was produced, who generated it and whether it has been independently evaluated.

They also need to distinguish the existence of evidence from the conclusion that evidence can sustain. An artifact may establish one condition without demonstrating a broader control objective. A technical test may provide substantial evidence within a narrow scope while providing little information about systems outside that scope. An independent assessment may establish conformity against defined criteria without addressing risks that fall outside the engagement.

These distinctions are necessary if evidence is to be interpreted accurately.

GRC platforms are likely to become more capable as automation, artificial intelligence and enterprise data integration continue to develop. They will collect larger volumes of information, monitor more conditions and provide increasingly sophisticated risk analytics. Risk Publishing’s 2026 buyer’s guide already reflects this convergence, with leading platforms combining automated evidence collection, control monitoring, risk analytics, third-party risk functions and broader governance capabilities (Ekai, 2026a).

As these systems mature, the challenge will increasingly shift from collecting evidence to preserving its meaning.

For organizations investing in GRC technology, the objective should be to maintain a clear relationship between evidence, criteria, scope and conclusion. Technology can make risk information more accessible and structured, while independent evaluation determines what that information demonstrates against defined criteria and within established boundaries. When you next compare compliance management software, ask each vendor to show how its platform records the criterion, population, period and source behind every piece of evidence.

Maintaining that distinction gives organizations a more precise understanding of the assurance represented by their risk and compliance environments and reduces the risk of drawing broader conclusions than the evidence can reasonably sustain.

Common Pitfalls in Managing Compliance Evidence

Pitfall	Root cause	Remedy
Reporting dashboard status as control effectiveness	Aggregated indicators treated as assurance conclusions	Label each tile with its evidence type and coverage; reserve “effective” for tested controls
Assuming connector coverage equals control scope	Integration scope never reconciled to inventories	Quarterly population reconciliation, owned by the second line
Mapping one artifact to many requirements without analysis	Cross-framework mapping used as a shortcut	Record which part of each requirement the artifact addresses; test separately where criteria differ
Accepting vendor attestations at face value	Procurement treats a SOC 2 report as a checkbox	Review scope, period, exceptions, complementary user entity controls and auditor identity
Using point-in-time evidence for period claims	Evidence collected just before audit fieldwork	Schedule collection across the period and flag gaps automatically
Nobody tests the automated tests	GRC logic sits outside the audit universe	Bring integrations and test scripts into internal audit scope; document the test logic
Letting platform templates become your evidence	Speed pressure during a first certification	Require evidence generated from your own systems; treat templates as drafts, never as proof

Frequently Asked Questions About Compliance Evidence

What is compliance evidence?

Compliance evidence is any record, observation or test result used to support a conclusion that an organization meets a defined requirement, such as an ISO/IEC 27001 clause, a SOC 2 criterion or a regulatory obligation. It includes policies, system configurations, logs, attestations, test reports and audit opinions. ISO 19011 treats audit evidence as verifiable information that is relevant to the audit criteria. Its value depends on that relevance, the population and period it covers, and how independently and reliably it was produced.

Is evidence stored in a GRC platform audit-ready?

Not automatically. A GRC platform makes evidence retrievable and traceable, which saves auditors time, but auditors still judge relevance, sufficiency and reliability for themselves. Evidence becomes audit-ready when each artifact carries its criterion, scope, period, source and owner, and when the systems that produced it have tested controls. Without that metadata, a well-organized repository is still just a well-organized repository.

Can continuous control monitoring replace internal audit?

No. Continuous control monitoring gives the first and second lines early warning on selected control conditions, and that is valuable. Internal audit provides independent assurance, including assurance over the monitoring itself. An automated test written and run by management does not meet the independence bar the IIA Standards set. The better model uses monitoring to direct internal audit attention and uses internal audit to validate the monitoring logic.

What makes compliance evidence sufficient?

Sufficiency is a judgment about quantity and quality relative to a specific conclusion. Evidence is sufficient when it addresses the exact criterion, covers the in-scope population or a representative sample, spans the relevant period, comes from a source whose independence matches the conclusion being drawn, and rests on complete and accurate data. The five-question test above is a practical way to document that judgment so a reviewer can follow it.

How should we treat vendor SOC 2 reports after the Delve allegations?

Read them rather than file them. Check the audit firm’s identity and licensing, the system description and scope, the testing period, any qualified opinion or exceptions, carved-out subservice organizations and the complementary user entity controls you are expected to operate. For critical vendors, corroborate with a bridge letter, targeted follow-up questions or your own testing. Our guide to AI vendor risk assessment applies the same discipline to AI suppliers.

How often should automated evidence integrations be validated?

At a minimum, reconcile integration scope to asset and identity inventories quarterly, and re-validate the test logic whenever the source system, the connector or the control changes. Internal audit should test the logic of integrations supporting key controls at least once per audit cycle. Monitor silent integration failures continuously, because a connector that stops returning data can leave an old “pass” status on the dashboard for weeks.

Does ISO/IEC 42001 change what counts as compliance evidence for AI?

It adds new evidence categories rather than new rules of evidence. An AI management system needs records of AI system inventories, impact assessments, risk treatment decisions and performance monitoring, and the same sufficiency questions apply to each. Expect assessors to ask whether your AI inventory covers shadow and embedded AI, not only the systems procurement approved.

---

Want to know what your compliance evidence can actually prove? Risk Publishing helps risk, compliance and audit teams define evidence requirements, validate GRC integrations and build evidence-quality KRIs that stand up to auditors, customers and boards. Explore our risk advisory services or get in touch to discuss your GRC environment.

References

Boyens, J., Smith, A., Bartol, N., Winkler, K., Holbrook, A., & Fallon, M. (2024). Cybersecurity supply chain risk management practices for systems and organizations (NIST Special Publication 800-161 Rev. 1, Update 1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-161r1-upd1

Ekai, C. (2026a, April 27). Best GRC software: 2026 buyer’s guide. Risk Publishing. https://riskpublishing.com/best-grc-software-2026-buyers-guide-2026/

Ekai, C. (2026b, July 23). Third-party risk management framework: A step-by-step guide for 2026. Risk Publishing. https://riskpublishing.com/third-party-risk-management-framework-for-2026/

International Organization for Standardization. (2022). ISO/IEC 27001:2022: Information security, cybersecurity and privacy protection: Information security management systems: Requirements. https://www.iso.org/standard/27001

International Organization for Standardization. (2023). ISO/IEC 42001:2023: Information technology: Artificial intelligence: Management system. https://www.iso.org/standard/42001

Pascoe, C., Quinn, S., & Scarfone, K. (2024). The NIST Cybersecurity Framework (CSF) 2.0 (NIST Cybersecurity White Paper 29). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.CSWP.29

Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1